Multi-Turn Conversations and Prompts for an Internal Office

Data Characteristics for This Category

Policy retrieval data in the biomedical field primarily originates from internal corporate regulations, Standard Operating Procedures (SOPs), quality management system documents, compliance guidelines, and relevant legal texts. These documents have a relatively low update frequency, typically revised quarterly or annually. However, urgent updates may occur when policies or regulations change. Document structures are often hierarchical and chapter-based, including titles, main text, attachments, and revision history. Common fields include policy number, effective date, issuing department, scope, and revision version number. Content is usually stored in plain text or PDF format. Some documents may embed charts, but text content is central.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The hierarchical structure of policy documents requires multi-turn conversations to understand a user's focus on specific chapters or sub-clauses and precisely locate information in retrieval results. The low update frequency means the model does not require frequent retraining or knowledge base updates. However, urgent revisions necessitate quick synchronization of the latest versions to avoid providing outdated information. Metadata within documents, such as policy numbers and effective dates, requires prompt design to guide the model in using this information for filtering and sorting, improving retrieval efficiency. The predominantly plain text format aids text vectorization and semantic matching, but the absence of chart content may limit understanding of certain processes or diagrams.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`Recall count`	`8–12`	Ensures coverage of relevant policy clauses while avoiding excessive irrelevant content, balancing retrieval efficiency and accuracy.
`Similarity threshold`	`0.75–0.85`	Balances recall and precision, filtering out document segments with low semantic relevance.
`Rerank result count`	`3–5`	Optimizes results through a reranking model, focusing on the most relevant policy clauses to enhance user experience.
`Chunk size`	`400–600 characters`	Accommodates the paragraph length of policy documents, ensuring each segment contains sufficient context and avoids truncating critical information.
`maxContext`	`4096`	Supports longer multi-turn conversation histories, allowing the model to understand the user's evolving context and intent in policy retrieval.
`top_p`	`0.7–0.9`	Encourages the model to generate more diverse and creative responses, providing a more comprehensive perspective on policy interpretation.

Three Common Pitfalls

The model responds with "no relevant policies or clauses found" even when the knowledge base contains the corresponding content. This typically occurs when Similarity threshold is set too high or Recall count is too low, leading to the filtering of valid information with slightly lower relevance.
When a user asks about a specific policy's revision history or effective date, the model cannot provide a clear answer. This may be because the knowledge base did not retain or correctly extract the policy's metadata fields during document chunking, preventing the model from accessing this information.
After multiple turns of conversation, the model misunderstands the user's intent, providing policy suggestions irrelevant to the current topic. This usually happens when maxContext is insufficient, causing the model to lose context by failing to effectively remember previous conversation turns.

How to Verify Configuration

Ask multi-turn questions about core policy clauses. Observe if the model consistently provides accurate and coherent policy explanations from different questioning angles, and verify the relevance ranking in the returned results.
For policies in the knowledge base that include metadata (e.g., policy number, effective date), simulate user questions to verify if the model can correctly extract and respond with this information.
Gradually increase the number of conversation turns and try changing the focus of the questions at critical points. Observe if the model maintains an understanding of the current policy retrieval task and adjusts its retrieval strategy accordingly.
Use the FastGPT backend debugging tool to check the actual retrieval results under the influence of Recall count and Similarity threshold for each conversation, confirming they align with expectations.

Note: The values provided are common starting points. They should be measured against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.